Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)
DC Field | Value | Language |
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dc.contributor.author | Khan, Muhammad Fahad | - |
dc.contributor.author | Aadil, Farhan | - |
dc.contributor.author | Maqsood, Muazzam | - |
dc.contributor.author | Bukhari, Syed Hashim Raza | - |
dc.contributor.author | Hussain, Maqbool | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.date.accessioned | 2021-08-11T11:23:43Z | - |
dc.date.available | 2021-08-11T11:23:43Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/5348 | - |
dc.description.abstract | A network of wirelessly connected vehicles by using any mean of connectivity is termed as the Internet of Vehicle (IoV). Managing this type of network is a challenging task. Clustering is a technique to efficiently manage resources in this type of network. In a cluster, all inter/intra cluster communication is managed by a cluster head (CH). Load on each CH, the lifetime of the cluster and the total number of clusters in a network are some parameters to measure the efficiency of the network. In this paper, a novel technique based on moth flame clustering algorithm for IoV (MFCA-IoV) is proposed. Moth flame optimizer is a nature-inspired algorithm. MFCA-IoV generates optimized clusters for robust transmission and is evaluated experimentally with renowned techniques. These techniques are Grey-Wolf-optimization-based method used for the clustering called as GWOCNETs, multi-objective particle-swarm-optimization (MOPSO), clustering algorithm based on Ant colony optimization for vehicular ad-hoc networks termed as CACONET and comprehensive learning particle-swarm-optimization (CLPSO). To assess the comparative efficiency of these algorithms, numerous experiments are performed. The parameters like network grid-size, number of nodes, speed, direction, and transmission-range of the nodes are considered for optimized clustering. The results indicate, MFCA-IoV is showing 73% nodes, which are not selected as a cluster head while existing techniques are providing 57%, 50%, 51%, and 58% for GWOCNETs, CLPSO, MOPSO, and CACONET, respectively. Hence, lesser the nodes are selected as CH, the more optimal result will be considered. | - |
dc.format.extent | 17 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV) | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2018.2886420 | - |
dc.identifier.scopusid | 2-s2.0-85058665260 | - |
dc.identifier.wosid | 000457753900001 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.7, pp 11613 - 11629 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 7 | - |
dc.citation.startPage | 11613 | - |
dc.citation.endPage | 11629 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | PARTICLE SWARM OPTIMIZATION | - |
dc.subject.keywordPlus | AD-HOC NETWORKS | - |
dc.subject.keywordPlus | GLOBAL OPTIMIZATION | - |
dc.subject.keywordPlus | MOBILE | - |
dc.subject.keywordPlus | EVOLUTION | - |
dc.subject.keywordPlus | SEARCH | - |
dc.subject.keywordAuthor | Internet of Vehicle (IoV) | - |
dc.subject.keywordAuthor | vehicular ad-hoc networks (VANETs) | - |
dc.subject.keywordAuthor | intelligent transportation system (ITS) | - |
dc.subject.keywordAuthor | Ant-colony-optimization (ACO) | - |
dc.subject.keywordAuthor | particle swarm optimization (PSO) | - |
dc.subject.keywordAuthor | MFO | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | meta-heuristic algorithms | - |
dc.subject.keywordAuthor | population-based algorithm | - |
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